def demo():
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
from BR import BR
set_printoptions(precision=3, suppress=True)
X, Y = make_XOR_dataset()
N, L = Y.shape
print("CLASSIFICATION")
h = linear_model.SGDClassifier(n_iter=100)
nn = ELM(8, f=tanh, h=BR(-1, h))
nn.fit(X, Y)
# test it
print(nn.predict(X))
print("vs")
print(Y)
print("REGRESSION")
r = ELM(100, h=linear_model.LinearRegression())
r.fit(X, Y)
print(Y)
print(r.predict(X))
print("REGRESSION OI")
r = ELM_OI(100, h=BR(-1, h=linear_model.SGDRegressor()))
r.fit(X, Y)
print(Y)
print(r.predict(X))
def demo():
from tools import make_XOR_dataset
X,Y = make_XOR_dataset()
N,L = Y.shape
nn = DRBM(20)
nn.train(X, Y)
# test it
print nn.predict(X)
print "vs"
print Y
def demo():
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
nn = DRBM(20)
nn.train(X, Y)
# test it
print nn.predict(X)
print "vs"
print Y
def demo():
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
br = BR(L, linear_model.SGDClassifier(n_iter=100))
br.fit(X, Y)
# test it
print(br.predict(X))
print("vs")
print(Y)
def demo():
import sys
sys.path.append( '../core' )
from tools import make_XOR_dataset
X,Y = make_XOR_dataset()
N,L = Y.shape
br = BR(L, linear_model.SGDClassifier(n_iter=100))
br.fit(X, Y)
# test it
print br.predict(X)
print "vs"
print Y
def demo():
#from molearn.core.tools import make_XOR_dataset
import sys
sys.path.append( '../core' )
from tools import make_XOR_dataset
X,Y = make_XOR_dataset()
N,L = Y.shape
cc = RCC(L, SGDClassifier(n_iter=100))
cc.fit(X, Y)
# test it
print cc.predict(X)
print "vs"
print Y
def demo():
#from molearn.core.tools import make_XOR_dataset
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
cc = RCC(L, SGDClassifier(n_iter=100))
cc.fit(X, Y)
# test it
print(cc.predict(X))
print("vs")
print(Y)
def demo():
#from molearn.core.tools import make_XOR_dataset
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
ps = PS()
ps.fit(X, Y)
# test it
print(ps.predict(X))
print("vs")
print(Y)
def demo():
from tools import make_XOR_dataset
from BR import BR
X,Y = make_XOR_dataset()
N,L = Y.shape
h = linear_model.SGDClassifier(n_iter=100)
nn = ELM(8,BR(L,h))
nn.train(X, Y)
# test it
print nn.predict(X)
print "vs"
print Y
def demo():
#from molearn.core.tools import make_XOR_dataset
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
lp = LP()
lp.fit(X, Y)
# test it
print(lp.predict_proba(X))
print("vs")
print(Y)
def demo():
import sys
sys.path.append('../core')
from tools import make_XOR_dataset
X, Y = make_XOR_dataset()
N, L = Y.shape
from sklearn import linear_model
h = linear_model.LogisticRegression()
h = linear_model.SGDClassifier(n_iter=100)
ml = ML(L, h)
ml.fit(X, Y)
# Eval
print(ml.predict(X))
print("vs")
print(Y)
def demo():
import sys
sys.path.append( '../core' )
from tools import make_XOR_dataset
X,Y = make_XOR_dataset()
N,L = Y.shape
from sklearn import linear_model
h_ = linear_model.SGDClassifier(n_iter=100)
from CC import RCC
cc = RCC(h=h_)
e = Ensemble(n_estimators=10,base_estimator=cc)
e.fit(X, Y)
# test it
print(e.predict(X))
print("vs")
print(Y)
